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A Multi-Point Meso–Micro Downscaling Method Including Atmospheric Stratification

Author

Listed:
  • Renko Buhr

    (ForWind-Center for Wind Energy Research, University of Oldenburg, Küpkersweg 70, D-26129 Oldenburg, Germany)

  • Hassan Kassem

    (Fraunhofer Institute for Wind Energy Systems (IWES), Küpkersweg 70, D-26129 Oldenburg, Germany)

  • Gerald Steinfeld

    (ForWind-Center for Wind Energy Research, University of Oldenburg, Küpkersweg 70, D-26129 Oldenburg, Germany)

  • Michael Alletto

    (Wobben Research and Development (WRD), Teerhof 59, D-28199 Bremen, Germany)

  • Björn Witha

    (ForWind-Center for Wind Energy Research, University of Oldenburg, Küpkersweg 70, D-26129 Oldenburg, Germany
    Current address: Energy & Meteo Systems GmbH, Oskar-Homt-Str. 1, D-26131 Oldenburg, Germany.)

  • Martin Dörenkämper

    (Fraunhofer Institute for Wind Energy Systems (IWES), Küpkersweg 70, D-26129 Oldenburg, Germany)

Abstract

In wind energy site assessment, one major challenge is to represent both the local characteristics as well as general representation of the wind climate on site. Micro-scale models (e.g., Reynolds-Averaged-Navier-Stokes (RANS)) excel in the former, while meso-scale models (e.g., Weather Research and Forecasting (WRF)) in the latter. This paper presents a fast approach for meso–micro downscaling to an industry-applicable computational fluid dynamics (CFD) modeling framework. The model independent postprocessing tool chain is applied using the New European Wind Atlas (NEWA) on the meso-scale and THETA on the micro-scale side. We adapt on a previously developed methodology and extend it using a micro-scale model including stratification. We compare a single- and multi-point downscaling in critical flow situations and proof the concept on long-term mast data at Rödeser Berg in central Germany. In the longterm analysis, in respect to the pure meso-scale results, the statistical bias can be reduced up to 45% with a single-point downscaling and up to 107% (overcorrection of 7%) with a multi-point downscaling. We conclude that single-point downscaling is vital to combine meso-scale wind climate and micro-scale accuracy. The multi-point downscaling is further capable to include wind shear or veer from the meso-scale model into the downscaled velocity field. This adds both, accuracy and robustness, by minimal computational cost. The new introduction of stratification in the micro-scale model provides a marginal difference for the selected stability conditions, but gives a prospect on handling stratification in wind energy site assessment for future applications.

Suggested Citation

  • Renko Buhr & Hassan Kassem & Gerald Steinfeld & Michael Alletto & Björn Witha & Martin Dörenkämper, 2021. "A Multi-Point Meso–Micro Downscaling Method Including Atmospheric Stratification," Energies, MDPI, vol. 14(4), pages 1-22, February.
  • Handle: RePEc:gam:jeners:v:14:y:2021:i:4:p:1191-:d:504117
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    References listed on IDEAS

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